The United States real estate market is currently facing its worst hit in two decades due to the slowdown of housing sales. The most affected by this decline are real estate investors and home developers who are currently struggling to break-even financially on their investments. For these investors, it is of utmost importance to evaluate the current status of the market and predict its performance over the shortterm in order to make appropriate financial decisions. This paper presents the development of artificial neural network based models to support real estate investors and home developers in this critical task. The paper describes the decision variables, design methodology, and the implementation of these models. The models utilize historical market performance data sets to train the artificial neural networks in order to predict unforeseen future performances. An application example is analyzed to demonstrate the model capabili-ties in analyzing and predicting the market performance. The model testing and validation showed that the error in prediction is in the range between -2% and +2%. The United States real estate market is currently facing its worst hit in two decades due to the slowdown of housing sales. The most affected by this decline are real estate investors and home developers who are currently struggling to break-even financially on their investments. For these investors, it is utmost importance to evaluate the current status of the market and predict its performance over the shortterm in order to make appropriate financial decisions. This paper presents the development of artificial neural network based models to support real estate investors and home developers. The paper describes the decision tasks, design methodology, and the implementation of these models. The models utilize historical market performance data sets to train the artificial neural networks in order to predict unforeseen future performances. An application example is analyzed to demonstrate the model capabili-ties in analyzing and predicting the market perform ance. The model testing and validation showed that the error in prediction is in the range between -2% and + 2%.